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Personal profile
Research profile
Juseong Lee is an Assistant Professor at the Eindhoven University of Technology (TU/e). He is a member of the Operations, Planning, Accounting, and Control (OPAC), the Department of Industrial Engineering and Innovation Science. His research focuses on integrating data-driven models and artificial intelligence into industrial processes. Specifically, he is developing predictive maintenance approaches for various engineering systems. He is also interested in applying new methodologies, such as digital twins, multi-objective optimization, and deep learning, for real decision-making in business. Juseong's research aims to improve the reliability, efficiency, and sustainability of Industry 4.0.
Academic background
Juseong Lee received his PhD in Aerospace Engineering from the Faculty of Aerospace Engineering at Delft University of Technology (2022). The title of his PhD dissertation is “Predictive Aircraft Maintenance: Integrating Remaining-Useful-Life Prognostics into Maintenance Optimization.” Dr. Lee received his MSc in Aerospace Engineering (2018) and BSc in Aerospace Engineering (2016) with Magna Cum Laude from the Korea Advanced Institute of Science and Technology.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 9 Industry, Innovation, and Infrastructure
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Data-Driven Endpoint Detection for Small Open Area Etching Using Interpretable Transfer Learning
Wang, Z., Kim, J., Han, S., Akcay, A., Chae, H. (Corresponding author) & Lee, J. (Corresponding author), Feb 2026, In: IEEE Transactions on Semiconductor Manufacturing. 39, 1, p. 74-81 8 p., 11299816.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile5 Downloads (Pure) -
Diagnostics for Mechanical Systems with Unknown Fault Modes: A Novel Open Set Recognition Approach
Song, J., Lee, J., Fecarotti, C. & Houtum, van, G. J. J. A. N., 3 Jul 2026, PHM Society European Conference. Do, P., Ezhilarasu, C. & Bregon, A. (eds.). PHM Society, 10 p. (Proceedings of the European Conference of the PHM Society; vol. 9, no. 1).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
Open AccessFile9 Downloads (Pure) -
Uncertainty-aware fault mode diagnostics for condition-based maintenance of bearing
Lee, J. & Fecarotti, C., 2026, Proceedings of the European Safety and Reliability Conference, ESREL2026. Matos, J. C., Lourenço, P. B. & Beer, M. (eds.). Research Publishing, Singapore, p. 1373-1380 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
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Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification
Lee, J. (Corresponding author), Mitici, M., Blom, H. A. P., Bieber, P. & Freeman, F., Feb 2023, In: Aerospace. 10, 2, 17 p., 186.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile21 Link opens in a new tab Citations (Scopus)171 Downloads (Pure) -
Deep reinforcement learning for predictive aircraft maintenance using probabilistic Remaining-Useful-Life prognostics
Lee, J. (Corresponding author) & Mitici, M., Feb 2023, In: Reliability Engineering and System Safety. 230, 14 p., 108908.Research output: Contribution to journal › Article › Academic › peer-review
Open Access204 Link opens in a new tab Citations (Scopus)
Courses
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Maintenance and service logistics
Basten, R. J. I., Lee, J., Tanil-Can, I. & Song, J. 1/09/13 → 31/08/27
Course
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Quality and reliability engineering
Lee, J., Solano Blanco, A., Fecarotti, C. & Song, J. 1/09/13 → 31/08/27
Course
Press/Media
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Delft University of Technology Researcher Releases New Study Findings on Information Technology (Analyzing Emerging Challenges for Data-Driven Predictive Aircraft Maintenance Using Agent-Based Modeling and Hazard Identification)
Blom, P. P. M. & Lee, J.
2/03/23
1 item of Media coverage
Press/Media: Expert Comment
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Reports Outline Engineering Study Results from Delft University of Technology (Deep Reinforcement Learning for Predictive Aircraft Maintenance Using Probabilistic Remaining-useful-life Prognostics)
8/02/23
1 item of Media coverage
Press/Media: Expert Comment